**6. Discussion**

We have proposed a novel MCSCNN–LSTM that models different domain and multi-scale feature patterns to forecast the electricity consumption at different durations. The difficulty of feature extraction for different durations forecasts using one model, and different duration electricity consumptions have different patterns of the trend as shown in Figure 3, which requires a model with excellent feature extraction capacity with good robustness. Besides, collecting related data such as weather, the temperature is costly and time-consumption. Therefore, we developed MCSCNN–LSTM to extract multi-scale and multi-domain features by only inputting the electricity history data, as shown in Figure 2. We connected CNN and LSTM parallelly with dual inputs, Table 10 and Figure 6 shows it is more effective than conventional stacked CNN–LSTM.

We compared our proposed deep model with other excellent deep models in Table 6, which indicates our proposed model is stable. Furthermore, Tables 7–9 and Figure 4 show that we have improved the performance compared to the stable DNN [34] and the best results of NPCNN [35], LSTM [20], and CNN–LSTM [25]. Primarily, it has improved a lot for STF, MTF, and LTF. Figure 5 explained that the proposed method could predict the detailed irregular patterns of electricity consumption.

As can be seen from Table 10, we have analyzed the feature capacity of each part of the proposed MCSCNN–LSTM by comparing the averaged MAPE. In addition, we computed the averaged improvement ratio of the proposed MCSCNN–LSTM by using MCSCNN as the benchmark in Figure 6. It proved that each part of our proposed model has excellent feature extraction capacity. Moreover, we have analyzed the inside feature map of MCSCNN–LSTM as shown in Figure 7, it shows the CNN part of MCSCNN–LSTM can extract multi-scale robust global features, and statistic components are more effective in extracting detailed patterns than LSTM.

As shown in Figure 8, we have designed comparative experiments on three data sets to validate the transfer learning capacity of the proposed MCSCNN–LSTM. The findings from Figure 8 have proven that our proposed deep model has excellent transfer learning skills. In order to quantify the transfer learning capacity, we compared the *p*-value of the *t*-test with none-transfer learning methods in Table 12. Moreover, we have confirmed the proposed method could accurately forecast multi-step electricity consumption in advance in Table 13 and Figure 9, the results from Table 13 and Figure 9 indicate our proposed method outperforms CNN–LSTM which was developed in [27].
